Pattern recognition algorithm for identifying and quantifying single and mixed contaminants in air with an array of nanomaterial-based gas sensors

ABSTRACT

A method is described for identifying and quantifying single and mixed contaminants in air by reading nanohybrid gas sensors multivariate output and processing it inside the algorithm. The algorithm analyzes sensor signal in real time and outputs estimated values for concentrations of target gases.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application claims priority to U.S. Provisional Patent ApplicationNo. 62/721,289, filed Aug. 22, 2018, U.S. Provisional Patent ApplicationNo. 62/721,293, filed Aug. 22, 2018, U.S. Provisional Patent ApplicationNo. 62/721,296, filed Aug. 22, 2018, U.S. Provisional Application No.62/721,302, filed Aug. 22, 2018, U.S. Provisional Patent Application No.62/721,306, filed Aug. 22, 2018, U.S. Provisional Patent Application No.62/721,309, filed Aug. 22, 2018, U.S. Provisional Application No.62/721,311, filed Aug. 22, 2018, U.S. Provisional Patent Application No.62/799,466, filed Jan. 31, 2019, the contents of which are incorporatedherein by reference.

BACKGROUND 1. Technical Field

The embodiments described herein relate generally to systems and methodsfor measuring an analyte gas and mixtures in air, and more particularly,to systems and methods for simultaneous gas mixture concentrationsmeasurement with an array of nanomaterial-based gas sensors.

2. Related Art

Commercially available gas sensors can be cumbersome to use, expensiveand limited in performance (e.g. accuracy, selectivity, lowest detectionlimit, etc.). In addition, other major drawbacks may include inabilityto detect different types of gases at the same time, inability tomeasure absolute concentration of individual gases, the requirement forfrequent re-calibration, a size incompatible with integration into smallform factor systems such as wearable devices, the reliance onpower-hungry techniques such as heating or on technologies not wellsuited to manufacturing in very high volume.

The ability to accurately detect multiple gases at the same time, oftenat parts-per-billion (PPB) sensitivity is becoming crucial to a growingnumber of industries as well as to the world-wide expansion of airquality monitoring initiatives aiming to address household and urban airpollution challenges.

SUMMARY

A nano gas sensor architecture that delivers key fundamental attributesrequired for the broad deployment of sensors capable of low detectionlimits (PPB) in support of highly granular collection of gas informationin ambient air is described herein.

According to one aspect, a method for the selective detection of atarget gas and measuring the concentration values comprising: takingresistance values of 8, 16, 32, 64, or 128 channels of nanohybrid gassensors sampled every 80, 120, 160, or 200 milliseconds; filtering outthe high frequency noise using an exponential average low pass filter;computing the rate of sensor response change; and evaluating sensorresponse with respect to other sensor channels including the temperaturesensor.

According to another aspect, a method for tracking null referencebaseline using multiple-channel time series signal from a hybridnanostructure gas sensor, comprising: taking resistance values ofmultiple channels of nanohybrid gas sensors; comparing them against thereference resistance values benchmarked in ambient atmosphere with knownconcentrations of contributing gases; and adjusting the starting valuesfor target gas concentrations using the deviations from benchmarkedvalues for at least some of temperature, humidity and multiple channelsof nanohybrid gas sensors.

BRIEF DESCRIPTION OF DRAWINGS

These and other features, aspects, and embodiments are described belowin the section entitled “Detailed Description.”

FIG. 1 illustrates the basic principles to construct a gas sensor;

FIG. 2 is a prospective view of a physical implementation of a hybridnanostructure gas sensing element in accordance with one embodiment;

FIG. 3 is a diagram illustrating an embodiment of a gas sensor arraythat can be included in the hybrid nanostructure gas sensing element ofFIG. 2;

FIG. 4 is a block diagram of the hybrid nanostructure gas sensor systemthat incorporates the hybrid nanostructure gas sensing element of FIG. 2in accordance with one embodiment;

FIG. 5 is a chart showing the flow of gas information through the hybridnanostructure gas sensor system of FIG. 4;

FIG. 6 is an exploded view of an example wearable product built around aPCB embodiment of the hybrid nanostructure gas sensor system of FIG. 4;

FIG. 7 is a block diagram illustrating an example wired or wirelesssystem that can be used in connection with various embodiments describedherein;

FIG. 8 is a graph illustrating the filtering out of high frequency noiseusing an exponential average low pass filter in accordance with oneembodiment; and

FIG. 9 is a diagram illustrating an example process for predictingsettled resistance value for transient material response to changing gasconcentration in accordance with one embodiment.

DETAILED DESCRIPTION

Embodiments for a hybrid nanostructure gas sensing system are describedherein. The disclosure and the various features and advantageous detailsthereof are explained more fully with reference to the non-limitingembodiments and examples that are described and/or illustrated in theaccompanying drawings and detailed in the following. It should be notedthat the features illustrated in the drawings are not necessarily drawnto scale, and features of one embodiment may be employed with otherembodiments as the skilled artisan would recognize, even if notexplicitly stated herein. Descriptions of well-known components andprocessing techniques may be omitted so as to not unnecessarily obscurethe embodiments of the disclosure. The examples used herein are intendedmerely to facilitate an understanding of ways in which the disclosuremay be practiced and to further enable those of skill in the art topractice the embodiments of the disclosure. Accordingly, the examplesand embodiments herein should not be construed as limiting the scope ofthe disclosure. Moreover, it is noted that like reference numeralsrepresent similar parts throughout the several views of the drawings.

The architecture embodied in the hybrid nanostructure gas sensing systemdescribed hrein achieves the basic requirement of selectivelyidentifying the presence of a gas analyte in diverse mixtures of ambientair but it is also designed to identify multiple gases at the same time,to be compatible in terms of size and power with very small form factors(including for mobile and wearable applications), to be easy toIntegrate in IoT applications and to be self-calibrating, thusunshakling the application and/or the service provider from the burdenand expense of regular re-calibration.

FIG. 1 describes the basic ingredients for a successful gas sensor 100.As can be seen, such a sensor includes a sensing element 102 that iscreated by depositing a sensitive layer 104 over a substrate 106. Thesensing element 102 can then interact with gaseous chemical compounds108 altering one or more electrical properties of the sensing element102. The change in electrical properties can be detected by feeding thesensor raw signals 110 through specially designed signal processingelectronics 112. The resulting response signals 114 can be measured andquantified directly or through the application of pattern recognitiontechniques.

The embodiments described herein comprise six basic elements. The firstis the basic sensor element or sensing channel, which combines astructural component, built on a substrate suitable for reliablehigh-volume manufacturing, with a deposited electrolyte containinghybrid nano structures in suspension. The formulation of the electrolyteis specific to a particular gas or family of gases. A silicon substrate106 and the structural component can be built using a MEMS manufacturingprocess. The structural component is essentially an unfinishedelectrical circuit between two electrodes. The deposition of theelectrolyte completes the electrical circuit and, when biased andexposed to gas analytes, changes to one or more of the electricalcharacteristics of the circuit are used to detect and measure gases.

The second element is the arrangement of multiple sensing channels intoan array structure specifically designed and optimized to interface withdata acquisition electronics 112. The array structure, combined with theuse of pattern recognition algorithms, makes it possible to detectmultiple gases at the same time with a single sensor by customizing oneor more of the individual sensing channels in the array for a specificgas or family of gases while using other sensing channels to facilitatesuch critical functions as selectivity.

FIG. 2 is a conceptual view of a hybrid nanostructure physical sensingelement 102 in accordance with one example embodiment. Differentmaterials can be used for the substrate 106 on which the rest of thesensing element 102 is constructed. But from the perspective of veryhigh volume manufacturing, silicon technology can be preferred andspecifically MEMS technology, which provides the necessary foundationfor a customer-defined set of manufacturing steps with the flexibilityto modulate the complexity of the process based on the sophistication ofthe sensor chip being built, e.g., to support further innovation or toaddress special product needs. Silicon technology also provides accessto time-proven test methods and multiple sources of Automated TestEquipment that can be customized to fit the needs of gas sensingtechnology.

The sensing element 102 is made of an incomplete or “open” electricalcircuit between two electrodes 202, which is then completed or “closed”by depositing, a molecular formulation electrolyte 204 with hybridnanostructures 208 in suspension. The process is compatible with severalcommonly used deposition techniques but does require speciallycustomized equipment and proprietary techniques to achieve the desiredquality and reproducibility in a high-volume manufacturing environment.In certain embodiments, the sensing element 102 can be speciallypatterned to support efficient deposition of nanomaterial in pico-litteramounts and to facilitate incorporation of multiple elements into anarray to enables the design of multi-gas sensors.

Electrodes 202 can then be bonded to bonding pads 206 in order tocommunicate signals 110 to the rest of the system.

One or more molecular formulations may be necessary to completely andselectively identify a particular gas. Combining multiple sensingelements 102, each capable of being “programmed” with a uniqueformulation, into a sensor array provides the flexibility necessary todetect and measure multiple gases at the same time. It also enables richfunctional options such as for instance measuring humidity, an importantfactor to be accounted for in any gas sensor design, directly on thesensor chip (after all water vapor is just another gas). Another exampleis the combination for the same gas or family of gases of a formulationcapable of very fast reaction to the presence of the gas while anotherformulation, slower acting, may be used for accurate concentrationmeasurement; this would be important in applications where a very fastwarning to the presence of a dangerous substance is required but actualaccurate concentration measurement may not be needed at the same time(e.g. first responders in an industrial emergency situation).

FIG. 3 illustrates the preferred embodiment of a multichannel, gassensor array 305 where a silicon substrate 302 is used with a MEMSmanufacturing process to build the structure of the sensing channels onwhich the molecular formulations 204 can be deposited. For illustrationpurposes the size of the individual sensor die 304 is shown as beingmuch larger than achievable in practice; a single 8″ wafer 300 willtypically yield several thousand multi-gas capable sensor chips. Anarray 305 of sensing elements 102 is implemented on a single die 304 andeach wafer 300 yields several thousand dies, or chips 304. Each sensingelement 102 can then be functionalized by depositing a specificmolecular formulation 204 thereon.

Thus, after MEMS manufacturing, additional steps are required tocomplete the fabrication of each sensing element 102. First, molecularformulations 204 are deposited and cured using specialized equipment.This happens at wafer level and the equipment is designed in a modularfashion to allow for the scaling of the output of a manufacturingfacility by duplicating modules and fabrication processes in acopy-exactly fashion. After completion of the manufacturing steps, thewafers 300 must be singulated using a clean dicing technology in orderto prevent damage to the sensing elements 102. An example of suchtechnology is Stealth dicing.

The third element is the electronic transducer that detects changes inthe electrical characteristics of the sensor array 305, provides signalconditioning and converts the analog signal from the sensor elements 102into a digital form usable by the data acquisition system, described inmore detail below. The transducer can be a low voltage analog circuitthat provides biasing to the array of sensing channels and twofunctional modes: parking and measurement. Sensing channels are inparking mode either when not in measurement mode or when notused/enabled at all for a given application. The circuitry is designedto maintain the sensing channels in a linear region of operation, tooptimize power consumption, to enable any combination of channels ineither parking or measurement modes and to provide a seamless transitionbetween modes.

FIG. 5 shows the basic flow of information through a complete nano gassensor system, such as system 400 described in more detail below. Whenthe sensor array 305 is exposed to the mixture of gas analytes 108 inits environment, in step 502, the sensitive layers 104 of the materialsdeposited on the sensor elements 102, or sensing channels react,according to their formulation 202, to the presence of specificcomponent gases in the mixture. The reaction causes a change in theelectrical characteristics of the sensing channels 102, which iscaptured by the transducer in the electronics sub-system, in step 504,and then analyzed by the pattern recognition system programmed in thesub-system MCU, in step 506. The output is an absolute value of theconcentration of the gases being detected. This is then combined, instep 508, with other desirable meta-data such as time or geo-locationinto a digital record. This digital record (or a portion of it) canoptionally be displayed locally in step 510 (for example, in the case ofa wearable application where the sensor is paired to a phone, the datacan be further manipulated and displayed by a specially written mobileapplication running on the phone). More importantly the data isuploaded, via a mechanism that is dependent on the application, to aCloud data platform in step 512, where the data can be normalized instep 514 and accessed via various application in step 516.

The fourth element is a MCU-based data acquisition and measurementengine, which also provides additional functions such as overall sensorsystem management and communication, as necessary with encryption, toand from a larger system into which the sensor is embedded.

The third and fourth elements are designed to work together and to forma complete electronic sub-system specifically tuned to work with thearray of sensing channels 305 implemented as a separate component. Thetransducer 404 is firmware configurable to provide optimal A/Dconversion for a pattern recognition system running on the MCU 406 andimplementing the gas detection and measurement algorithm(s).

The electronic sub-system 402 is suitable for implementation in avariety of technologies depending on target use model and technical/costtrade-offs. PCB implementations will enable quick turn-around and thedeclination of a family of related products (for instance with differentcommunication interfaces) to support multiple form factors andapplications with the same core electronics. When size andpower/performance trade-offs are critical, the electronic sub-system 402is implemented as a System On a Chip (SoC), which can then be integratedtogether with a MEMS chip carrying the array of sensing channels 305into a System In a Package (SIP).

The sensor die 304 must then be assembled with the sensor's electronicsub-system to complete the hybrid nanostructure gas sensor 400 for whicha functional block diagram is shown in FIG. 4.

The electronic sub-system can be implemented as a PCB or as a SoC. Ifthe PCB route is followed the sensor die 304 can be either wire-bondedto the electronic sub-system 402 board after completion of the PCBAssembly (PCBA) step or, if the sensor die 304 has itself beenindividually assembled in a SMT package, it can be soldered on the boardas part of PCBA. If the SoC route is followed, the sensor die togetherwith the SoC die of the electronic sub-system 402 can be stacked andassembled together into a single package (System In a Package) or eachcan possibly be assembled into individual packages.

Either assembled into its own package or assembled into a SIP, thesensor chip 304 must be exposed to ambient air. Therefore, the packagelid must include a hole of sufficient size over the sensor.

Testing happens at various points of the sensor manufacturing process.

After sensor functionalization (deposition of the molecular formulations204), certain handling precautions must be followed for the rest of theproduct manufacturing flow to prevent accidental damage to the sensorchip 304 (e.g. a pick and place tool must not make contact with thesurface of the sensing elements).

The fifth element is the gas detection and measurement algorithm. Thealgorithm implements a method for predicting target gas concentration byreading the hybrid nanostructure sensor array's multivariate output andprocessing it inside the algorithm. The algorithm analyzes sensorsignals in real time and outputs estimated values for concentrations oftarget gases. The algorithm development is based on models that arespecific to the materials deposited on the sensing channels of thesensor array. These models are trained based on the collection of anabundant volume of data in the laboratory (multiple concentrations oftarget gases, combinations of gases, various values of temperature,relative humidity and other environmental parameters). Sophisticatedsupervised modeling techniques are used to attain the best possibleagreement between true and predicted values of target gasconcentrations. Prior to deployment, extensive lab and field testing iscarried out to optimize model performance and finalize sensorvalidation.

In certain embodiments, the algorithm can use exponential average lowpass filtering to ensure efficient memory management and fast processingspeeds. FIG. 8 is a graph illustrating the filtering of the highfrequency noise using the exponential average low pass filter. The highfrequency component is depicted as plot 802, while the filtered signalis plotted as line 804.

FIG. 9 is a diagram illustrating the computation of settled resistancevalue estimate for transient material response to changing gasconcentration. First, in step 902, the resistance rate for each channelis computed. The value of resistance rate for each channel is then takenas a byproduct of the exponential average low pass filter and multipliedby the material time constant to evaluate the transient resistance instep 904. The time constant is the measured property of the materialresponse to the target gas. The settled resistance estimate, which is asum of the transient resistance and a current resistance value is thendetermined in step 906.

Thus, in certain embodiments, a method for the selective detection of atarget gas and measuring the concentration values comprises processingthe resistance values of 8, 16, 32, 64, or 128 channels of nanohybridgas sensors sampled every 80, 120, 160, or 200 milliseconds andfiltering out the high frequency noise using the exponential average lowpass filter illustrated in FIG. 8. This is then followed by signalprocessing such as: computing the rate of sensor response change; andevaluating sensor response in relation to other sensor channelsincluding a temperature sensor channel.

Predicting settled sensor resistance values, as shown in FIG. 9, canthen be used to estimate algorithm input values when sensor outputvalues are in transition following the change in gas concentrationvalues. This is done in order to accelerate target gas concentrationpredictions without the need for waiting a long time to reachequilibrium in interaction between the sensor material and changing gas.

A gas model can then be used to relate change in resistance of materialsegments to target gas concentration via model coefficients. Therelation between sensor response and change in target gas concentrationis described by the equation:

C ^(i)=Σ_(j)α_(j) ^(i)(R ^(j) −R ^(j) ₀)/R ^(j) ₀ +C ^(i) ₀.

R^(j) ₀ is defined as the channel resistance for material j right beforethe exposure, R^(j) is defined as the resistance right after theexposure. The sum is taken over all channels of various materials jcontributing to the algorithm input.

C^(i) ₀ is defined as the target gas i concentration right before theexposure, C^(i) is defined as the target gas i concentration right afterexposure. For every target gas i each material j channel containscertain material-gas coefficient value α_(j) ^(i).

Preprocessed signals from nanohybrid gas sensor channels can then begrouped into segments each representing a specific material deposited onsensor channel. Multiple segments can be used in engaging a singletarget gas model. Multiple model concurrently executed in the algorithmpredicting concentration values for gases, such as: NO2, CO, O3, CH2O,CH4, etc.

Response of a sensor is a result of exposure to multiple gasconstituents in the atmosphere as well as the reaction of the sensor tovarious environmental factors such as humidity, temperature, pressure,and air flow. The algorithm resolves this cross-sensitivity complexityvia an over-constrained system of modeling equations. Compensationcoefficients to account for environmental factors are: i. humiditycompensation coefficient; ii temperature compensation coefficient; andiii pressure and air flow compensation coefficient.

The optimal solution to the system of equations is the output of thealgorithm containing the concentration values for target gases.

In certain embodiments, a method for tracking null reference baselineusing multiple-channel time series signal from a hybrid nanostructuregas sensor comprises taking resistance values of multiple channels ofnanohybrid gas sensors and comparing them against the referenceresistance values benchmarked in ambient atmosphere with knownconcentrations of contributing gases. The deviations from benchmarkedvalues can then be used to adjust the starting values for target gasconcentrations. The adjustment process uses inputs from temperature,humidity and multiple channels of nanohybrid gas sensors.

The first five elements together constitute the hybrid nanostructure gassensor 400 and provide all the functionality necessary to detectmultiple gases 108 in ambient air at the same time and to report theirabsolute concentrations. The sensing capability of the hybridnanostructure sensor array 305 is always “on” and the gas detection andmeasurement algorithm makes it possible for the sensor 400 to require nospecial calibration step before use and to remain self-calibratingthrough its operational life.

The sixth element is the Cloud Data Platform that enables a virtuallyunlimited number of sensors 400 deployed as part of a virtuallyunlimited number of applications to be hosted in a global database wherebig data techniques can be used to analyze, query and visualize theinformation to infer actionable insight. The use of a Cloud-basedenvironment provides all the necessary flexibility to customize how thedata can be partitioned, organized, protected and accessed based on therights of individual tenants.

The Cloud data platform provides another layer of sophistication to thesystem by allowing Cloud applications to operate on the data set. Forinstance, sensors 400 that are located in the same vicinity wouldtypically report consistent gas values thus allowing errant results tobe identified and a possible malfunction of one node of a network ofsensors investigated.

The continuous collection of highly granular gas information by amultitude of connected devices (IoT—Internet Of Things) is critical togo beyond monitoring to generate actionable insight from large amount ofcollected data (Big Data Analytics, Artificial Intelligence).

A few application examples are highlighted below.

Example 1

We take 20,000 breaths every day and the air we breathe impacts ourhealth—the science is already clear on this—but we rarely know what isin the air we breathe. To take meaningful action, consumers, scientists,public officials and business owners need the ability to measure airpollution at a personal, local and granular level which has, before thisinvention, been impossible due to the limitations of commerciallyavailable gas sensors mentioned above.

Mounting evidence suggests that prenatal and early life exposure tocommon environmental toxins, such as air pollution from fossil fuels,can cause lasting damage to the developing human brain. These effectsare especially pronounced in highly vulnerable fetuses, babies, andtoddlers as most of the brain's structural and functional architectureis established during these early developmental periods. Thesedisruptions to healthy brain development can cause various cognitive,emotional, and behavioral problems in later infancy and childhood.

The sensor technology described herein allows researchers to gatherhighly detailed, accurate data about pregnant women's exposure toenvironmental air pollution and the resulting effects on the developingbrain. The availability of this technology will represent a profoundadvance on current methods and efforts in the field that will havefar-reaching consequences for improving newborn and child healththroughout the world.

More generally, personal air monitoring and local indoor and outdoormonitoring will be a breakthrough for scientific research, healthcareinterventions, personal preventive actions, advocacy and more.

The sensor technology described herein can deliver complete processingand gas results to a broad spectrum of smart systems under developmentfor the Smart Cities of tomorrow. The sensor is designed for Plug andPlay integration into IoT devices and the small form factor iscompatible with a multitude of devices from LED lights to smart meters,to standalone monitoring stations, to non-stationary devices (drones,public vehicles, wearables, phones, etc.).

Example 2

The sensor technology described herein can be used in smart appliancessuch as connected refrigerators, that will help customers monitor foodfreshness, detect spoilage and the presence of harmful pesticideresidues. The simultaneous, multi-gas, sensing capability of theinvention will enable sensors that can recognize the gas patternsassociated with the condition of specific foods.

Example 3

A network or grid of the sensors 400 described herein, can be integratedinto industrial areas such as petrochemical complexes and oil refineriesto allow companies to monitor the sites during regular operation (e.g.for leaks) or in the event of natural or human-made disasters. Thesensors can also be installed in drones for data collection in hard toreach or potentially dangerous area. The ability of the technology to bedeployed in wearables and in fixed and mobile networks will provide bothpersonal protection and granular data across large area, allow theconstant monitoring of a facility for preventive measures to be taken ina timely fashion, save critical time when urgent decision making isrequired and provide invaluable information to protect workers andemergency personnel.

The same technology can place powerful new tools in the hands of firstresponders and officials responsible for public safety and homelandsecurity.

FIG. 6 shows an example product 600, in this case a battery-poweredwearable device, with the sensor 400 implemented as a small PCB. Thesensor technology lends itself to integration into any number of IoTdevices. While the sensor does not need the active creation of anairflow to function, the sensitive layers 104 at the surface of thesensor must be exposed to ambient air and at the same time provided areasonable amount of protection from dust and fluids. This is usuallyachieved by designing an air interface that ensures that the sensor 400is behind a perforated shield (e.g. the lid of an enclosure) with a thinmembrane (PTFE, 0.5 um mesh) being used to provide splash and dustprotection. Outdoor applications may require the design of a morecomplicated air interface to meet the weather-proofing requirements.

FIG. 7 is a block diagram illustrating an example wired or wirelesssystem 550 that can be used in connection with various embodimentsdescribed herein. For example the system 550 can be used as or inconjunction with one or more of the platforms, devices or processesdescribed above, and may represent components of a device, such assensor 400, the corresponding backend or cloud server(s), and/or otherdevices described herein. The system 550 can be a server or anyconventional personal computer, or any other processor-enabled devicethat is capable of wired or wireless data communication. Other computersystems and/or architectures may be also used, as will be clear to thoseskilled in the art.

The system 550 preferably includes one or more processors, such asprocessor 560. Additional processors may be provided, such as anauxiliary processor to manage input/output, an auxiliary processor toperform floating point mathematical operations, a special-purposemicroprocessor having an architecture suitable for fast execution ofsignal processing algorithms (e.g., digital signal processor), a slaveprocessor subordinate to the main processing system (e.g., back-endprocessor), an additional microprocessor or controller for dual ormultiple processor systems, or a coprocessor. Such auxiliary processorsmay be discrete processors or may be integrated with the processor 560.Examples of processors which may be used with system 550 include,without limitation, the Pentium® processor, Core i7® processor, andXeon® processor, all of which are available from Intel Corporation ofSanta Clara, Calif. Example processor that can be used in system 400include the ARM family of processors and the new open source RISC-Vprocessor architecture.

The processor 560 is preferably connected to a communication bus 555.The communication bus 555 may include a data channel for facilitatinginformation transfer between storage and other peripheral components ofthe system 550. The communication bus 555 further may provide a set ofsignals used for communication with the processor 560, including a databus, address bus, and control bus (not shown). The communication bus 555may comprise any standard or non-standard bus architecture such as, forexample, bus architectures compliant with industry standard architecture(ISA), extended industry standard architecture (EISA), Micro ChannelArchitecture (MCA), peripheral component interconnect (PCI) local bus,or standards promulgated by the Institute of Electrical and ElectronicsEngineers (IEEE) including IEEE 488 general-purpose interface bus(GPIB), IEEE 696/S-100, and the like.

System 550 preferably includes a main memory 565 and may also include asecondary memory 570. The main memory 565 provides storage ofinstructions and data for programs executing on the processor 560, suchas one or more of the functions and/or modules discussed above. Itshould be understood that programs stored in the memory and executed byprocessor 560 may be written and/or compiled according to any suitablelanguage, including without limitation C/C++, Java, JavaScript, Pearl,Visual Basic, .NET, and the like. The main memory 565 is typicallysemiconductor-based memory such as dynamic random access memory (DRAM)and/or static random access memory (SRAM). Other semiconductor-basedmemory types include, for example, synchronous dynamic random accessmemory (SDRAM), Rambus dynamic random access memory (RDRAM),ferroelectric random access memory (FRAM), and the like, including readonly memory (ROM).

The secondary memory 570 may optionally include an internal memory 575and/or a removable medium 580, for example a floppy disk drive, amagnetic tape drive, a compact disc (CD) drive, a digital versatile disc(DVD) drive, other optical drive, a flash memory drive, etc. Theremovable medium 580 is read from and/or written to in a well-knownmanner. Removable storage medium 580 may be, for example, a floppy disk,magnetic tape, CD, DVD, SD card, etc.

The removable storage medium 580 is a non-transitory computer-readablemedium having stored thereon computer executable code (i.e., software)and/or data. The computer software or data stored on the removablestorage medium 580 is read into the system 550 for execution by theprocessor 560.

In alternative embodiments, secondary memory 570 may include othersimilar means for allowing computer programs or other data orinstructions to be loaded into the system 550. Such means may include,for example, an external storage medium 595 and an interface 590.Examples of external storage medium 595 may include an external harddisk drive or an external optical drive, or and external magneto-opticaldrive.

Other examples of secondary memory 570 may include semiconductor-basedmemory such as programmable read-only memory (PROM), erasableprogrammable read-only memory (EPROM), electrically erasable read-onlymemory (EEPROM), or flash memory (block oriented memory similar toEEPROM). Also included are any other removable storage media 580 andcommunication interface 590, which allow software and data to betransferred from an external medium 595 to the system 550.

System 550 may include a communication interface 590. The communicationinterface 590 allows software and data to be transferred between system550 and external devices (e.g. printers), networks, or informationsources. For example, computer software or executable code may betransferred to system 550 from a network server via communicationinterface 590. Examples of communication interface 590 include abuilt-in network adapter, network interface card (NIC), PersonalComputer Memory Card International Association (PCMCIA) network card,card bus network adapter, wireless network adapter, Universal Serial Bus(USB) network adapter, modem, a network interface card (NIC), a wirelessdata card, a communications port, an infrared interface, an IEEE 1394fire-wire, or any other device capable of interfacing system 550 with anetwork or another computing device.

Communication interface 590 preferably implements industry promulgatedprotocol standards, such as Ethernet IEEE 802 standards, Fiber Channel,digital subscriber line (DSL), asynchronous digital subscriber line(ADSL), frame relay, asynchronous transfer mode (ATM), integrateddigital services network (ISDN), personal communications services (PCS),transmission control protocol/Internet protocol (TCP/IP), serial lineInternet protocol/point to point protocol (SLIP/PPP), and so on, but mayalso implement customized or non-standard interface protocols as well.

Software and data transferred via communication interface 590 aregenerally in the form of electrical communication signals 605. Thesesignals 605 are preferably provided to communication interface 590 via acommunication channel 600. In one embodiment, the communication channel600 may be a wired or wireless network, or any variety of othercommunication links. Communication channel 600 carries signals 605 andcan be implemented using a variety of wired or wireless communicationmeans including wire or cable, fiber optics, conventional phone line,cellular phone link, wireless data communication link, radio frequency(“RF”) link, or infrared link, just to name a few.

Computer executable code (i.e., computer programs or software) is storedin the main memory 565 and/or the secondary memory 570. Computerprograms can also be received via communication interface 590 and storedin the main memory 565 and/or the secondary memory 570. Such computerprograms, when executed, enable the system 550 to perform the variousfunctions of the present invention as previously described.

In this description, the term “computer readable medium” is used torefer to any non-transitory computer readable storage media used toprovide computer executable code (e.g., software and computer programs)to the system 550. Examples of these media include main memory 565,secondary memory 570 (including internal memory 575, removable medium580, and external storage medium 595), and any peripheral devicecommunicatively coupled with communication interface 590 (including anetwork information server or other network device). Thesenon-transitory computer readable mediums are means for providingexecutable code, programming instructions, and software to the system550.

In an embodiment that is implemented using software, the software may bestored on a computer readable medium and loaded into the system 550 byway of removable medium 580, I/O interface 585, or communicationinterface 590. In such an embodiment, the software is loaded into thesystem 550 in the form of electrical communication signals 605. Thesoftware, when executed by the processor 560, preferably causes theprocessor 560 to perform the inventive features and functions previouslydescribed herein.

In an embodiment, I/O interface 585 provides an interface between one ormore components of system 550 and one or more input and/or outputdevices. Example input devices include, without limitation, keyboards,touch screens or other touch-sensitive devices, biometric sensingdevices, computer mice, trackballs, pen-based pointing devices, and thelike. Examples of output devices include, without limitation, cathoderay tubes (CRTs), plasma displays, light-emitting diode (LED) displays,liquid crystal displays (LCDs), printers, vacuum florescent displays(VFDs), surface-conduction electron-emitter displays (SEDs), fieldemission displays (FEDs), and the like.

The system 550 also includes optional wireless communication componentsthat facilitate wireless communication over a voice and over a datanetwork. The wireless communication components comprise an antennasystem 610, a radio system 615 and a baseband system 620. In the system550, radio frequency (RF) signals are transmitted and received over theair by the antenna system 610 under the management of the radio system615.

In one embodiment, the antenna system 610 may comprise one or moreantennae and one or more multiplexors (not shown) that perform aswitching function to provide the antenna system 610 with transmit andreceive signal paths. In the receive path, received RF signals can becoupled from a multiplexor to a low noise amplifier (not shown) thatamplifies the received RF signal and sends the amplified signal to theradio system 615.

In alternative embodiments, the radio system 615 may comprise one ormore radios that are configured to communicate over various frequencies.In one embodiment, the radio system 615 may combine a demodulator (notshown) and modulator (not shown) in one integrated circuit (IC). Thedemodulator and modulator can also be separate components. In theincoming path, the demodulator strips away the RF carrier signal leavinga baseband receive audio signal, which is sent from the radio system 615to the baseband system 620.

If the received signal contains audio information, then baseband system620 decodes the signal and converts it to an analog signal. Then thesignal is amplified and sent to a speaker. The baseband system 620 alsoreceives analog audio signals from a microphone. These analog audiosignals are converted to digital signals and encoded by the basebandsystem 620. The baseband system 620 also codes the digital signals fortransmission and generates a baseband transmit audio signal that isrouted to the modulator portion of the radio system 615. The modulatormixes the baseband transmit audio signal with an RF carrier signalgenerating an RF transmit signal that is routed to the antenna systemand may pass through a power amplifier (not shown). The power amplifieramplifies the RF transmit signal and routes it to the antenna system 610where the signal is switched to the antenna port for transmission.

The baseband system 620 is also communicatively coupled with theprocessor 560. The central processing unit 560 has access to datastorage areas 565 and 570. The central processing unit 560 is preferablyconfigured to execute instructions (i.e., computer programs or software)that can be stored in the memory 565 or the secondary memory 570.Computer programs can also be received from the baseband processor 610and stored in the data storage area 565 or in secondary memory 570, orexecuted upon receipt. Such computer programs, when executed, enable thesystem 550 to perform the various functions of the present invention aspreviously described. For example, data storage areas 565 may includevarious software modules (not shown).

Various embodiments may also be implemented primarily in hardware using,for example, components such as application specific integrated circuits(ASICs), or field programmable gate arrays (FPGAs). Implementation of ahardware state machine capable of performing the functions describedherein will also be apparent to those skilled in the relevant art.Various embodiments may also be implemented using a combination of bothhardware and software.

Furthermore, those of skill in the art will appreciate that the variousillustrative logical blocks, modules, circuits, and method stepsdescribed in connection with the above described figures and theembodiments disclosed herein can often be implemented as electronichardware, computer software, or combinations of both. To clearlyillustrate this interchangeability of hardware and software, variousillustrative components, blocks, modules, circuits, and steps have beendescribed above generally in terms of their functionality. Whether suchfunctionality is implemented as hardware or software depends upon theparticular application and design constraints imposed on the overallsystem. Skilled persons can implement the described functionality invarying ways for each particular application, but such implementationdecisions should not be interpreted as causing a departure from thescope of the invention. In addition, the grouping of functions within amodule, block, circuit or step is for ease of description. Specificfunctions or steps can be moved from one module, block or circuit toanother without departing from the invention.

Moreover, the various illustrative logical blocks, modules, functions,and methods described in connection with the embodiments disclosedherein can be implemented or performed with a general purpose processor,a digital signal processor (DSP), an ASIC, FPGA or other programmablelogic device, discrete gate or transistor logic, discrete hardwarecomponents, or any combination thereof designed to perform the functionsdescribed herein. A general-purpose processor can be a microprocessor,but in the alternative, the processor can be any processor, controller,microcontroller, or state machine. A processor can also be implementedas a combination of computing devices, for example, a combination of aDSP and a microprocessor, a plurality of microprocessors, one or moremicroprocessors in conjunction with a DSP core, or any other suchconfiguration.

Additionally, the steps of a method or algorithm described in connectionwith the embodiments disclosed herein can be embodied directly inhardware, in a software module executed by a processor, or in acombination of the two. A software module can reside in RAM memory,flash memory, ROM memory, EPROM memory, EEPROM memory, registers, harddisk, a removable disk, a CD-ROM, or any other form of storage mediumincluding a network storage medium. An exemplary storage medium can becoupled to the processor such the processor can read information from,and write information to, the storage medium. In the alternative, thestorage medium can be integral to the processor. The processor and thestorage medium can also reside in an ASIC.

Any of the software components described herein may take a variety offorms. For example, a component may be a stand-alone software package,or it may be a software package incorporated as a “tool” in a largersoftware product. It may be downloadable from a network, for example, awebsite, as a stand-alone product or as an add-in package forinstallation in an existing software application. It may also beavailable as a client-server software application, as a web-enabledsoftware application, and/or as a mobile application.

While certain embodiments have been described above, it will beunderstood that the embodiments described are by way of example only.Accordingly, the systems and methods described herein should not belimited based on the described embodiments. Rather, the systems andmethods described herein should only be limited in light of the claimsthat follow when taken in conjunction with the above description andaccompanying drawings.

What is claimed is:
 1. A method for the selective detection of a targetgas and measuring the concentration values comprising: taking resistancevalues of 8, 16, 32, 64, or 128 channels of nanohybrid gas sensorssampled every 80, 120, 160, or 200 milliseconds; filtering out the highfrequency noise using the exponential average low pass filter; computingthe rate of sensor response change; and evaluating sensor response withrespect to other sensor channels including the temperature sensor. 2.The method of claim 1, further comprising predicting settled sensorresistance values to estimate algorithm input values when sensor outputvalues are in transition following the change in gas concentrationvalues.
 3. The method of claim 2, further comprising using a gas modelthat relates change in resistance of material segments to target gasconcentration via model coefficients, wherein the relation betweensensor response and change in target gas concentration described byequation:C ^(i)=Σ_(j)α_(j) ^(i)(R ^(j) −R ^(j) ₀)/R ^(j) ₀ +C ^(i) ₀; WhereinR^(j) ₀ is defined as the channel resistance for material j right beforethe exposure, R^(j) is defined as the resistance right after theexposure, and wherein the sum is taken over all channels of variousmaterials j contributing to the algorithm input; and C^(i) ₀ is definedas the target gas i concentration right before the exposure, C^(i) isdefined as the target gas i concentration right after exposure, whereinfor every target gas i each material j channel contains certainmaterial-gas coefficient value α_(j) ^(i).
 4. The method of claim 1,wherein preprocessed signals from nanohybrid gas sensor channels aregrouped into segments each representing a specific material deposited onsensor channel, and wherein multiple segments are used in engaging asingle target gas model.
 5. The method of claim 4, wherein multiplemodels are concurrently executed in the algorithm predictingconcentration values for gases, including at least one of NO2, SO2, CO,CO2, O3, CH2O, CH4, NH3, N20, organic compounds such as Acetone andEthanol, and various Hydrocarbons.
 6. The method of claim 1, wherein aresponse of a sensor is a result of exposure to multiple gasconstituents in the atmosphere as well as the reaction of the sensor tovarious environmental factors such as humidity, temperature, pressureand air flow, and further comprising resolving the cross-sensitivitycomplexity via an over-constrained system of modeling equations.
 7. Themethod of claim 6, wherein the compensation coefficients to account forenvironmental factors are a combination of: humidity compensationcoefficient, temperature compensation coefficient, and pressure and airflow compensation coefficient.
 8. A method for tracking null referencebaseline using multiple-channel time series signal from a hybridnanostructure gas sensor, comprising: taking resistance values ofmultiple channels of nanohybrid gas sensors; comparing them against thereference resistance values benchmarked in ambient atmosphere with knownconcentrations of contributing gases; and adjusting the starting valuesfor target gas concentrations using the deviations from benchmarkedvalues for at least some of temperature, humidity and multiple channelsof nanohybrid gas sensors.